Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations present, the description carries the behavioral burden. It usefully discloses case-insensitive matching, cell-level containment, and the 50-result cap, which go beyond the input schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence delivers the core behavior with no fluff. The distinguishing details—case-insensitivity, cell matching, and limit—are front-loaded and each one earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool, the description covers the essential behavior: what is searched, what is returned, and the result cap. It would be slightly stronger with an explicit default-limit note, but nothing critical is missing for invoking a basic search.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%, leaving the 'limit' parameter undocumented in the schema. The description partially compensates with 'up to 50' and clarifies query behavior, but it does not explain the default limit or how the limit interacts with the cap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states exactly what the tool does: returns rows of the BioBricks dataset whose cells contain the query, case-insensitive, capped at 50. This clearly separates it from sibling tools like dataset_stats or dataset_row.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use is implied: use this when you need text search across dataset cells. However, it does not explicitly say when not to use it or name alternatives, leaving some selection reasoning to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.